A Federated Graph-Based Multimodal AI Framework for Privacy-Preserving and Explainable Osteoporosis Detection

Osteoporosis is a progressive skeletal disorder characterized by decreased Bone Mineral Density (BMD) and structural deterioration, leading to an increased risk of fractures. Traditional diagnostic methods, such as Dual-Energy X-ray Absorptiometry (DXA) and the Fracture Risk Assessment Tool (FRAX), often fail to detect osteoporosis at an early stage due to their limited sensitivity and accessibility. Recent advancements in Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML) have shown significant potential in improving osteoporosis detection. However, existing models face challenges related to data privacy, explainability, computational complexity, and generalizability across diverse populations. This study introduces Federated Graph-based Multimodal Osteoporosis Diagnosis (FGMOD), a novel AI-driven framework that integrates Federated Learning (FL) and Graph Neural Networks (GNNs) for enhanced osteoporosis risk assessment. Unlike traditional DL models that rely on centralized training, FL ensures privacy-preserving learning across multiple hospitals and institutions. Simultaneously, GNNs model trabecular bone connectivity, offering deeper insights into microarchitectural changes in osteoporotic bones. Multimodal data fusion, incorporating structured clinical data, DXA scans, and biochemical markers, significantly improves prediction accuracy and interpretability. The proposed FGMOD model was validated on a real-world dataset comprising 10 000 patient records, 5000 DXA scans, and biochemical profiles. Comparative evaluation with state-of-the-art AI models demonstrated superior performance, achieving 94.2% accuracy, 93.4% precision, 93.1% recall, and a ROC-AUC score of 0.96. Unlike CNN-based models, which rely heavily on high-resolution images, FGMOD effectively extracts clinically relevant features from graph-based bone connectivity analysis, ensuring robust and interpretable predictions. Furthermore, the use of SHAP-based feature importance analysis provides clinicians with an explainable AI decision-making process, enhancing trust in the model’s recommendations. Performance comparisons against CNN, Gradient Boosting, Random Forest, and Transfer Learning models revealed that FGMOD outperformed all baselines, particularly in privacy-aware federated training, osteoporosis risk stratification, and real-time inference on edge AI devices. Additionally, FGMOD reduced inference latency to 45 ms per sample, making it suitable for real-time clinical deployment. FGMOD represents a significant advancement in AI-powered osteoporosis detection, addressing key challenges such as privacy, generalizability, and interpretability. Future research should focus on expanding FL collaborations across global hospitals, integrating wearable sensor data for continuous bone health monitoring, and enhancing GNN-based osteoporosis modeling for faster inference on mobile healthcare platforms. This study paves the way for clinically reliable, privacy-preserving, and scalable AI solutions in osteoporosis risk assessment and early detection.